Improving Implicit Discourse Relation Recognition with Natural Language Explanations from LLMs
arXiv:2602.21763v1 Announce Type: new Abstract: Implicit Discourse Relation Recognition (IDRR) remains a challenging task due to the requirement for deep semantic understanding in the absence of explicit discourse markers. A further limitation is that existing methods only predict relations without providing any supporting explanations. Recent advances in large language models (LLMs) have shown strong reasoning capabilities in both deep language understanding and natural language explanation generation. In this work, we propose a simple yet effective approach to distill the reasoning capabilities of LLMs into lightweight IDRR models to improve both performance and interpretability. Specifically, we first prompt an LLM to generate explanations for each training instance conditioned on its gold label. Then, we introduce a novel classification-generation framework that jointly performs relation prediction and explanation generation, and train it with the additional supervision of LLM-gen
arXiv:2602.21763v1 Announce Type: new Abstract: Implicit Discourse Relation Recognition (IDRR) remains a challenging task due to the requirement for deep semantic understanding in the absence of explicit discourse markers. A further limitation is that existing methods only predict relations without providing any supporting explanations. Recent advances in large language models (LLMs) have shown strong reasoning capabilities in both deep language understanding and natural language explanation generation. In this work, we propose a simple yet effective approach to distill the reasoning capabilities of LLMs into lightweight IDRR models to improve both performance and interpretability. Specifically, we first prompt an LLM to generate explanations for each training instance conditioned on its gold label. Then, we introduce a novel classification-generation framework that jointly performs relation prediction and explanation generation, and train it with the additional supervision of LLM-generated explanations. Our framework is plug-and-play, enabling easy integration with most existing IDRR models. Experimental results on PDTB demonstrate that our approach significantly improves IDRR performance, while human evaluation further confirms that the generated explanations enhance model interpretability. Furthermore, we validate the generality of our approach on sentiment classification and natural language inference
Executive Summary
The article titled 'Improving Implicit Discourse Relation Recognition with Natural Language Explanations from LLMs' addresses the challenges of Implicit Discourse Relation Recognition (IDRR) by leveraging the advanced reasoning capabilities of large language models (LLMs). The authors propose a novel framework that integrates LLM-generated explanations into lightweight IDRR models, enhancing both performance and interpretability. The approach involves prompting an LLM to generate explanations for training instances and then using these explanations to train a classification-generation framework. Experimental results on the PDTB dataset demonstrate significant improvements in IDRR performance, and human evaluations confirm the enhanced interpretability of the models. The generality of the approach is also validated on sentiment classification and natural language inference tasks.
Key Points
- ▸ IDRR is challenging due to the lack of explicit discourse markers.
- ▸ LLMs can generate natural language explanations to improve IDRR performance.
- ▸ A novel classification-generation framework is proposed to integrate LLM-generated explanations.
- ▸ Experimental results show significant improvements in IDRR performance and interpretability.
- ▸ The approach is validated on sentiment classification and natural language inference tasks.
Merits
Enhanced Performance
The proposed framework significantly improves IDRR performance, as demonstrated by experimental results on the PDTB dataset.
Improved Interpretability
The integration of natural language explanations enhances the interpretability of IDRR models, making them more transparent and understandable.
Generality
The approach is validated on other NLP tasks such as sentiment classification and natural language inference, demonstrating its broad applicability.
Demerits
Dependency on LLMs
The approach relies heavily on the reasoning capabilities of LLMs, which may introduce biases or errors if the LLM-generated explanations are not accurate.
Computational Resources
Training and deploying LLMs can be computationally expensive, which may limit the accessibility of the proposed approach for smaller organizations or researchers with limited resources.
Generalization to Other Domains
While the approach shows promise, further validation is needed to ensure its effectiveness across a wider range of NLP tasks and datasets.
Expert Commentary
The article presents a significant advancement in the field of Implicit Discourse Relation Recognition by leveraging the reasoning capabilities of large language models. The proposed framework not only improves the performance of IDRR models but also enhances their interpretability through the integration of natural language explanations. This dual benefit is particularly valuable in applications where transparency and accuracy are paramount. The experimental results on the PDTB dataset and the validation on other NLP tasks demonstrate the robustness and generality of the approach. However, the dependency on LLMs and the computational resources required for training and deployment are notable limitations. Future research should focus on addressing these challenges and exploring the applicability of the framework across a broader range of NLP tasks and datasets. Overall, the article makes a substantial contribution to the field and sets a new benchmark for improving IDRR models.
Recommendations
- ✓ Further validation of the approach on diverse NLP tasks and datasets to ensure its broad applicability.
- ✓ Exploration of methods to reduce the computational resources required for training and deploying the proposed framework.
